scholarly journals GiST: A Stochastic Model for Generating Spatially and Temporally Correlated Daily Rainfall Data

2010 ◽  
Vol 23 (22) ◽  
pp. 5990-6008 ◽  
Author(s):  
Guillermo A. Baigorria ◽  
James W. Jones
Keyword(s):  
North Carolina ◽  
Correlation Coefficients ◽  
Daily Rainfall ◽  
Weather Generator ◽  
Rainfall Data ◽  
Random Numbers ◽  
Rainfall Events ◽  
Pairwise Correlation ◽  
Weather Generators ◽  
Weather Stations

Abstract Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed to generate realizations of daily rainfall for multiple sites in an area while preserving the spatial and temporal correlations among sites. A two-step method generates rainfall events at multiple sites followed by rainfall amounts at sites where generated rainfall events occur. The generation of rainfall events was based on a new orthogonal Markov chain for discrete distributions. For generating rainfall amounts, a vector of random numbers (from a uniform distribution), of order equal to the number of locations with rainfall events that were generated to occur in a day, was matrix-multiplied by the corresponding factorized correlation matrix to create spatially correlated random numbers. Elements from the resulting vector were transformed to a gamma distribution using cumulative probability functions for each location and rescaled to rainfall amounts. One study area was located in north-central Florida, where correlated rainfall data were generated for seven weather stations to evaluate its performance versus a widely used single-site weather generator. A second area was in North Carolina, where rainfall was generated for 25 weather stations to evaluate the effects of a larger number of stations in other regions. One thousand yearlong replications of daily rainfall data were generated for each area. Monthly spatial correlations of generated daily rainfall events and amounts among all pairs of weather stations closely matched their observed counterparts. For daily rainfall amounts the correlation coefficients between the observed pairwise correlation coefficients and the ones estimated from synthetic data among weather stations were 0.977 for Florida and 0.964 for North Carolina. The performance of the geospatial–temporal (GiST) weather generator was also analyzed by comparing the distributions of lengths of dry and wet spells, joint probabilities, Markov transitional probabilities, distance decay of correlation functions, and regionwide days without rainfall at any station. Multiannual mean and standard deviation of the number of rainy days per month and mean monthly rainfall were also calculated. All comparisons between observed and generated rainfall events and amounts using the GiST weather generator were highly correlated. The root-mean-square errors of pairwise correlation values ranged from 0.05 to 0.11 for rainfall events and from 0.03 to 0.06 for amounts.

scholarly journals Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

2020 ◽  
Vol 52 (2) ◽  
pp. 143
Author(s):  
Andung Bayu Sekaranom ◽  
Emilya Nurjani ◽  
Rika Harini ◽  
Andi Syahid Muttaqin
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Regression Function ◽  
Seasonal Prediction ◽  
Weather Generator ◽  
Rainfall Data ◽  
Rainfall Simulation ◽  
Rainfall Prediction ◽  
Student’S T

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.  

scholarly journals Trend and spatial variability of 1-day extreme rainfall from 1980 - 2015: study at the adminisitrative area of UPT PSDA Pasuruan

2019 ◽  
Vol 8 (1) ◽  
pp. 29
Author(s):  
Indarto Indarto
Keyword(s):  
Spatial Variability ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Statistical Test ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Significance Level ◽  
Main Input ◽  
Crossing Test

This study aims to analyze trends,  shift and spatial variability of extreme-rainfall in the area of UPT PSDA Pasuruan. The daily rainfall data from 64 stations from 1980 until 2015 were used as main input. The 1-day extreem rainfall data is determined as the maximum annual of 24-hour rainfall events.  The statistical  analysis using Mann-Kendall, Rank-Sum, and Median Crossing Test using significance level α = 0,05. The spatial variability of extrem rainfall data is described using average annual 24-hour rainfall during the periods of record. Each station is represented by one value. The values are then interpolated using IDW interpolation methods to maps the spatial variability of extreem rainfall event.  The results show the value of statistical test for each stations that show the existing  trend, shift, or randomness of data. The result also produce thematic maps show the spatial variability of extreme rainfall and the value of each trend.

scholarly journals Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia

Geosciences ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 43
Author(s):  
Md Masud Hasan ◽  
Barry F. W. Croke ◽  
Shuangzhe Liu ◽  
Kunio Shimizu ◽  
Fazlul Karim
Keyword(s):  
Probability Distribution ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Mean Square ◽  
Weibull Distributions ◽  
Rainfall Events ◽  
High Rainfall

Probabilistic models for sub-daily rainfall predictions are important tools for understanding catchment hydrology and estimating essential rainfall inputs for agricultural and ecological studies. This research aimed at achieving theoretical probability distribution to non-zero, sub-daily rainfall using data from 1467 rain gauges across the Australian continent. A framework was developed for estimating rainfall data at ungauged locations using the fitted model parameters from neighbouring gauges. The Lognormal, Gamma and Weibull distributions, as well as their mixed distributions were fitted to non-zero six-minutes rainfall data. The root mean square error was used to evaluate the goodness of fit for each of these distributions. To generate data at ungauged locations, parameters of well-fit models were interpolated from the four closest neighbours using inverse weighting distance method. Results show that the Gamma and Weibull distributions underestimate and lognormal distributions overestimate the high rainfall events. In general, a mixed model of two distributions was found better compared to the results of an individual model. Among the five models studied, the mixed Gamma and Lognormal (G-L) distribution produced the minimum root mean square error. The G-L model produced the best match to observed data for high rainfall events (e.g., 90th, 95th, 99th, 99.9th and 99.99th percentiles).

scholarly journals Evaluation the Best Random Component in Modified Thomas-Fiering Model in Generating Rainfall Data for Akre station

2019 ◽  
Vol 9 (2) ◽  
pp. 186-192
Author(s):  
Fawaz Kh. Aswad ◽  
Ali A. Yousif ◽  
Sayran A. Ibrahim
Keyword(s):  
Mean Squared Error ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rainfall Data ◽  
Random Numbers ◽  
Random Component ◽  
Chi Square ◽  
Daily Rainfall Data ◽  
Chi Square Test ◽  
Kolmogorov Smirnov

In this research, the effect of random component in the modified Thomas-Fiering model to generate daily rainfall data was studied, and Akre station considered a case study. A random component with special distributions: Normal random numbers, Wilson-Hilferty (W-H) transformation, truncated W-H, and Kirby modification to W-H transformation were used. The model applied to the daily rainfall data for Akre station for available years 2000–2006 and the model used to generate the rainfall data for the years 2006 and 2007. The results showed that the correlation coefficients between the observed and generated data were 0.82 for normal random numbers, 0.77 for W-H transformation, 0.89 for truncated –W –H, and 0.87 for KM to W-H transformation. The tests of Chi-square test, Kolmogorov–Smirnov test, root mean squared error (RMSE) test, and mean absolute error (MAE) test were used to compare between observed and generated data. All the results have passed the Chi-square test and Kolmogorov–Smirnov, where the calculated values were less than the tabulated value at 5% significance. For the test RMSE and MAE, the truncated W-H transform was the values of at least two. Therefore, W-H transform is the best for generating the rainfall data at Akre station

Enhanced capability to monitor drought using citizen weather stations

2021 ◽  
Author(s):  
Irene Garcia-Marti ◽  
Marijn de Haij ◽  
Hidde Leijnse ◽  
Jan Willem Noteboom ◽  
Aart Overeem ◽  
...  
Keyword(s):  
The Netherlands ◽  
Hydrological Cycle ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Added Value ◽  
Weather Data ◽  
Economic Sectors ◽  
Weather Observations ◽  
Drought Conditions ◽  
Weather Stations

<div> <div> <p>Recent studies indicate that global warming changes the global hydrological cycle and may trigger drought or expand and deepen existing drought conditions at our planet. During the summer of 2018 the Netherlands experienced extreme drought conditions, matching the previous drought record from 1976. This climatic extreme has been monitored using a cumulative metric based on the difference between (potential) evaporation and precipitation. In an effort to provide exhaustive drought monitoring facilities, the Netherlands Meteorological Service (KNMI) developed a drought monitor based on the Standard Precipitation Index (SPI) using 40 years of daily rainfall (1971-2010) from our official network of rain gauges for calibration. The daily SPI maps help decision makers to assess the status of meteorological drought in the Netherlands, thus enabling preventive measures mitigating its negative impacts on different socio-economic sectors. </p> </div> <div> <p>In the past two decades our global society has witnessed the advent of new technological and scientific advances that have reshaped the way we collect weather observations. Increasing numbers of citizens are joining the effort of monitoring the weather by installing citizen weather stations (CWS) in private spaces (e.g., home, schools), thus conforming novel sources of weather data. In 2015, the KNMI joined as a partner the Weather Observations Website (WOW) consortium, a citizen science initiative promoted by the UK Met Office bringing together weather enthusiasts all around the world. WOW-NL CWS have collected 100+ million observations between 2015-2019. However, it is still unclear how to use this remarkable volume of observations, or what is the added value (e.g., economic, operational, research) they provide with respect to the official network. </p> </div> <div> <p>In this ongoing work, we combined the newly developed SPI drought monitor with WOW observations from the Netherlands to obtain an ‘SPI-WOW’ indicator. Our goal is threefold: 1) illustrating how to turn WOW-NL data into operational value; 2) assessing the possibility of providing higher resolution drought maps including WOW-NL rainfall data; 3) enable the possibility for underrepresented regions to obtain (relevant) local drought metrics. </p> </div> <div> <p>We extracted 12 million precipitation observations for 2019 and, for each day of the year, we computed the daily rainfall accumulations for the previous 30 days (i.e., SPI-1). Note that the precipitation observations are not quality-controlled (QC). The calibrated model is tested with these newly created rainfall accumulations to obtain the SPI-WOW values. Our preliminary results compare the official vs alternative values of SPI at the location of each WOW-NL CWS. For each month we observe a moderate positive correlation, and there are CWS in the network capable of providing measurements close to the official ones. Further work to achieve the above-mentioned goal should include a) the application of a QC to the rainfall data to remove the outliers beforehand; b) thoroughly comparing the values of both networks in space and time across different scenarios; c) identifying the WOW-NL stations providing the best SPI metrics and its characteristics; d) assess the inclusion of radar data for the hi-res maps.</p> </div> </div>

ANALYSIS OF EXTREME RAINFALL INDICES IN PENINSULAR MALAYSIA

2016 ◽  
Vol 78 (9-4) ◽  
Author(s):  
Nur Shazwani Muhammad ◽  
Amieroul Iefwat Akashah ◽  
Jazuri Abdullah
Keyword(s):  
East Coast ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Peninsular Malaysia ◽  
Rainfall Data ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Daily Rainfall Data ◽  
Extreme Rain ◽  
Increasing Trends

Extreme rainfall events are the main cause of flooding. This study aimed to examine seven extreme rainfall indices, i.e. extreme rain sum (XRS), very wet day intensity (I95), extremely wet day intensity (I99), very wet day proportion (R95), extremely wet day proportion (R99), very wet days (N95) and extremely wet days (N99) using Mann-Kendall (MK) and the normalized statistic Z tests. The analyses are based on the daily rainfall data gathered from Bayan Lepas, Subang, Senai, Kuantan and Kota Bharu. The east coast states received more rainfall than any other parts in Peninsular Malaysia. Kota Bharu station recorded the highest XRS, i.e. 648 mm. The analyses also indicate that the stations in the eastern part of Peninsular Malaysia experienced higher XRS, I95, I99, R95 and R99 as compared to the stations located in the western and northern part of Peninsular Malaysia. Subang and Senai show the highest number of days for wet and very wet (N95) as compared to other stations. Other than that, all stations except for Kota Bharu show increasing trends for most of the extreme rainfall indices. Upward trends indicate that the extreme rainfall events were becoming more severe over the period of 1960 to 2014. 

scholarly journals A Stochastic Procedure for Temporal Disaggregation of Daily Rainfall Data in SuDS Design

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 403
Author(s):  
Matteo Pampaloni ◽  
Alvaro Sordo-Ward ◽  
Paola Bianucci ◽  
Ivan Gabriel-Martin ◽  
Enrica Caporali ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Design Parameters ◽  
Generation Process ◽  
Time Step ◽  
Rainfall Events ◽  
Daily Rainfall Data ◽  
Key Characteristics ◽  
Temporal Disaggregation

Hydrological design of Sustainable urban Drainage Systems (SuDS) is commonly achieved by estimating rainfall volumetric percentiles from daily rainfall series. Nevertheless, urban watersheds demand rainfall data at sub-hourly time step. Temporal disaggregation of daily rainfall records using stochastic methodologies can be applied to improve SuDS design parameters. This paper is aimed to analyze the ability of the synthetic rainfall generation process to reproduce the main characteristics of the observed rainfall and the estimation of the hydrologic parameters often used for SuDS design and by using the generally available daily rainfall data. Other specifics objectives are to analyze the effect of Minimum Inter-event Time (MIT) and storm volume threshold on rainfall volumetric percentiles commonly used in SuDS design. The reliability of the stochastic spatial-temporal model RainSim V.3 to reproduce observed key characteristics of rainfall pattern and volumetric percentiles, was also investigated. Observed and simulated continuous rainfall series with sub-hourly time-step were used to calculate four key characteristics of rainfall and two types of rainfall volumetric percentiles. To separate independent rainstorm events, MIT values of 3, 6, 12, 24, 48 and 72 h and storm volume thresholds of 0.2, 0.5, 1 and 2 mm were considered. Results show that the proposed methodology improves the estimation of the key characteristics of the rainfall events as well as the hydrologic parameters for SuDS design, compared with values directly deduced from the observed rainfall series with daily time-step. Moreover, MITs rainfall volumetric percentiles of total number of rainfall events are very sensitive to MIT and threshold values, while percentiles of total volume of accumulated rainfall series are sensitive only to MIT values.

Sign in / Sign up

Export Citation Format

Share Document